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Features Importance

Spearman Correlation of Models

Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- num_class: 4
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
2.7 seconds
Metric details
|
Extreme |
Major |
Minor |
Moderate |
accuracy |
macro avg |
weighted avg |
logloss |
| precision |
0 |
0 |
0.593171 |
0 |
0.593171 |
0.148293 |
0.351851 |
1.06944 |
| recall |
0 |
0 |
1 |
0 |
0.593171 |
0.25 |
0.593171 |
1.06944 |
| f1-score |
0 |
0 |
0.744642 |
0 |
0.593171 |
0.18616 |
0.441699 |
1.06944 |
| support |
223 |
647 |
2623 |
929 |
0.593171 |
4422 |
4422 |
1.06944 |
Confusion matrix
|
Predicted as Extreme |
Predicted as Major |
Predicted as Minor |
Predicted as Moderate |
| Labeled as Extreme |
0 |
0 |
223 |
0 |
| Labeled as Major |
0 |
0 |
647 |
0 |
| Labeled as Minor |
0 |
0 |
2623 |
0 |
| Labeled as Moderate |
0 |
0 |
929 |
0 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- num_class: 4
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
12.6 seconds
Metric details
|
Extreme |
Major |
Minor |
Moderate |
accuracy |
macro avg |
weighted avg |
logloss |
| precision |
0.624113 |
0.54522 |
0.851328 |
0.599572 |
0.756671 |
0.655058 |
0.742191 |
0.591085 |
| recall |
0.789238 |
0.652241 |
0.940907 |
0.301399 |
0.756671 |
0.670946 |
0.756671 |
0.591085 |
| f1-score |
0.69703 |
0.593948 |
0.893879 |
0.401146 |
0.756671 |
0.646501 |
0.736552 |
0.591085 |
| support |
223 |
647 |
2623 |
929 |
0.756671 |
4422 |
4422 |
0.591085 |
Confusion matrix
|
Predicted as Extreme |
Predicted as Major |
Predicted as Minor |
Predicted as Moderate |
| Labeled as Extreme |
176 |
43 |
4 |
0 |
| Labeled as Major |
88 |
422 |
53 |
84 |
| Labeled as Minor |
2 |
50 |
2468 |
103 |
| Labeled as Moderate |
16 |
259 |
374 |
280 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

SHAP Importance

SHAP Dependence plots
Dependence Extreme (Fold 1)

Dependence Major (Fold 1)

Dependence Minor (Fold 1)

Dependence Moderate (Fold 1)

SHAP Decision plots
Worst decisions for selected sample 1 (Fold 1)

Worst decisions for selected sample 2 (Fold 1)

Worst decisions for selected sample 3 (Fold 1)

Worst decisions for selected sample 4 (Fold 1)

Best decisions for selected sample 1 (Fold 1)

Best decisions for selected sample 2 (Fold 1)

Best decisions for selected sample 3 (Fold 1)

Best decisions for selected sample 4 (Fold 1)

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Summary of 3_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: multi:softprob
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: mlogloss
- num_class: 4
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
15.7 seconds
Metric details
|
Extreme |
Major |
Minor |
Moderate |
accuracy |
macro avg |
weighted avg |
logloss |
| precision |
0.683721 |
0.600634 |
0.89007 |
0.570248 |
0.777476 |
0.686168 |
0.770125 |
0.504764 |
| recall |
0.659193 |
0.585781 |
0.926039 |
0.519914 |
0.777476 |
0.672732 |
0.777476 |
0.504764 |
| f1-score |
0.671233 |
0.593114 |
0.907698 |
0.543919 |
0.777476 |
0.678991 |
0.77332 |
0.504764 |
| support |
223 |
647 |
2623 |
929 |
0.777476 |
4422 |
4422 |
0.504764 |
Confusion matrix
|
Predicted as Extreme |
Predicted as Major |
Predicted as Minor |
Predicted as Moderate |
| Labeled as Extreme |
147 |
68 |
2 |
6 |
| Labeled as Major |
60 |
379 |
24 |
184 |
| Labeled as Minor |
2 |
18 |
2429 |
174 |
| Labeled as Moderate |
6 |
166 |
274 |
483 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

SHAP Importance

SHAP Dependence plots
Dependence Extreme (Fold 1)

Dependence Major (Fold 1)

Dependence Minor (Fold 1)

Dependence Moderate (Fold 1)

SHAP Decision plots
Worst decisions for selected sample 1 (Fold 1)

Worst decisions for selected sample 2 (Fold 1)

Worst decisions for selected sample 3 (Fold 1)

Worst decisions for selected sample 4 (Fold 1)

Best decisions for selected sample 1 (Fold 1)

Best decisions for selected sample 2 (Fold 1)

Best decisions for selected sample 3 (Fold 1)

Best decisions for selected sample 4 (Fold 1)

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Summary of 4_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- num_class: 4
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
4.1 seconds
Metric details
|
Extreme |
Major |
Minor |
Moderate |
accuracy |
macro avg |
weighted avg |
logloss |
| precision |
0.672986 |
0.577778 |
0.879392 |
0.552258 |
0.766169 |
0.670603 |
0.756126 |
0.565377 |
| recall |
0.636771 |
0.602782 |
0.925658 |
0.46071 |
0.766169 |
0.65648 |
0.766169 |
0.565377 |
| f1-score |
0.654378 |
0.590015 |
0.901932 |
0.502347 |
0.766169 |
0.662168 |
0.759863 |
0.565377 |
| support |
223 |
647 |
2623 |
929 |
0.766169 |
4422 |
4422 |
0.565377 |
Confusion matrix
|
Predicted as Extreme |
Predicted as Major |
Predicted as Minor |
Predicted as Moderate |
| Labeled as Extreme |
142 |
74 |
3 |
4 |
| Labeled as Major |
60 |
390 |
27 |
170 |
| Labeled as Minor |
1 |
21 |
2428 |
173 |
| Labeled as Moderate |
8 |
190 |
303 |
428 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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Summary of 5_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: logloss
- num_class: 4
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
14.0 seconds
Metric details
|
Extreme |
Major |
Minor |
Moderate |
accuracy |
macro avg |
weighted avg |
logloss |
| precision |
0.643382 |
0.664368 |
0.87974 |
0.528541 |
0.768883 |
0.679008 |
0.762527 |
0.547393 |
| recall |
0.784753 |
0.446677 |
0.928708 |
0.538213 |
0.768883 |
0.674588 |
0.768883 |
0.547393 |
| f1-score |
0.707071 |
0.534196 |
0.903561 |
0.533333 |
0.768883 |
0.66954 |
0.761829 |
0.547393 |
| support |
223 |
647 |
2623 |
929 |
0.768883 |
4422 |
4422 |
0.547393 |
Confusion matrix
|
Predicted as Extreme |
Predicted as Major |
Predicted as Minor |
Predicted as Moderate |
| Labeled as Extreme |
175 |
35 |
2 |
11 |
| Labeled as Major |
81 |
289 |
22 |
255 |
| Labeled as Minor |
2 |
5 |
2436 |
180 |
| Labeled as Moderate |
14 |
106 |
309 |
500 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

SHAP Importance

SHAP Dependence plots
Dependence Extreme (Fold 1)

Dependence Major (Fold 1)

Dependence Minor (Fold 1)

Dependence Moderate (Fold 1)

SHAP Decision plots
Worst decisions for selected sample 1 (Fold 1)

Worst decisions for selected sample 2 (Fold 1)

Worst decisions for selected sample 3 (Fold 1)

Worst decisions for selected sample 4 (Fold 1)

Best decisions for selected sample 1 (Fold 1)

Best decisions for selected sample 2 (Fold 1)

Best decisions for selected sample 3 (Fold 1)

Best decisions for selected sample 4 (Fold 1)

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 3_Default_Xgboost |
4 |
| 4_Default_NeuralNetwork |
1 |
Metric details
|
Extreme |
Major |
Minor |
Moderate |
accuracy |
macro avg |
weighted avg |
logloss |
| precision |
0.691244 |
0.604615 |
0.889092 |
0.578624 |
0.780416 |
0.690894 |
0.772267 |
0.50403 |
| recall |
0.672646 |
0.607419 |
0.929089 |
0.506997 |
0.780416 |
0.679038 |
0.780416 |
0.50403 |
| f1-score |
0.681818 |
0.606014 |
0.90865 |
0.540448 |
0.780416 |
0.684232 |
0.775577 |
0.50403 |
| support |
223 |
647 |
2623 |
929 |
0.780416 |
4422 |
4422 |
0.50403 |
Confusion matrix
|
Predicted as Extreme |
Predicted as Major |
Predicted as Minor |
Predicted as Moderate |
| Labeled as Extreme |
150 |
66 |
2 |
5 |
| Labeled as Major |
59 |
393 |
23 |
172 |
| Labeled as Minor |
2 |
18 |
2437 |
166 |
| Labeled as Moderate |
6 |
173 |
279 |
471 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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